This course covers advanced topics
in computer vision in which vision problems are formulated and solved by
inference from noisy and uncertain data from statistical learning viewpoint. Topics
include learning algorithms and their applications to computer vision problems,
as well future research directions.
Lectures
·
CSIE building 105
·
6:00 to 8:45 pm
- Instructor:
Ming-Hsuan Yang (Please
call me Ming-Hsuan)
- Email:
mhyang @ csie.ntu.edu.tw
- Students
are encouraged to send me emails for questions and project discussions
- Office
hours: whenever I am at office
- Dimensionality
reduction (2.5 lectures, 09/28, 09/29, 10/03): principal component analysis, factor
analysis, probabilistic principal component analysis, mixture of
probabilistic principal component analyzers, mixture of factor analyzers, isomap, locally linear embedding.
- Classifier
(1.5 lectures, 10/03, 10/04): Fisher linear discriminant, support vector
machine, relevance vector machine, kernel methods, Adaboost.
- Generative
model (2 lectures, 10/19, 10/20): graphical model, Bayesian inference, belief
propagation, Gaussian process, EM algorithm.
- Approximate
inference (2 lectures, 10/24, 10/25): Markov chain Monte
Carlo, variational learning.
- Visual
tracking (2 lectures, 11/21, 11/22):
particle filter, mean shift, 2D/3D human tracking.
- Dynamics
(2 lectures, 11/23, 11/24): Autoregressive models, linear dynamic system,
Kalman filter, dynamic textures, video synthesis.
- Image
feature (1 lecture, 12/26): interest point, SIFT, exemplar.
- Object
detection (1 lecture, 12/27): face/car/pedestrian detection, human pose estimation.
- Other
topics (1 lecture, 12/28): regression, Markov random field, conditional
random field, convex optimization.
- Project
presentations (1 or 2 lectures, 12/29, 12/30).
- Check
the course web page for most recent update